The Effect of Womens Education on Terrorism

8/10/2019 The Effect of Womens Education on Terrorism

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The Effect of Womens Education on Terrorism:

Examining a Causal Chain Involving

Fertility and Young Male Populations

Deepa Bholanath Dhume

A senior thesis submitted to the

Department of Economics

in partial fulfillment of the requirements

for a degree of Bachelors of Arts with honors

Harvard College

Cambridge, Massachusetts


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Abstract

One quantifiable channel through which womens education may affect

terrorism is through the effect of womens education on fertility andpopulation demographics. Separate analyses on data from developing

countries and the Middle East/North Africa region show that increasing

womens education reduces fertility and eventually reduces the number of

young males in society. Prevailing views that a population with a largenumber of young males may be prone to supplying terrorists are supported

by a theoretical analysis of a rational choice model of the decision toparticipate in terrorist activities. However, evidence shows that areduction in the young male share of the population does not reduce

terrorism. This paper also uses fertility as an instrument for the native

young male share of the population and shows that the native young maleshare is also not related to terrorism in the developing world or in the

Middle East/North Africa region. One possible interpretation of these

findings is that, because the number of terrorists in a given population is

small compared to the pool of potential recruits, an increase in the supplyof total or native young males has no effect on the number of terrorists.

While there may be effects of womens education on terrorism through

political and social channels, womens education does not reduceterrorism through its effect on fertility and demographics.


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Table of Contents

1. Introduction 1

2. Literature Review 5

3. Economic Theory 10

4. Methods 21

5. Data Description 27

6. Results Developing Countries 36

7. Results Middle East and North Africa 43

8. Conclusion 50

References 53

Appendix: Summary and Regression Tables 55


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1. Introduction

To date, efforts to identify the determinants of terrorism have focused on religious

affiliation, education, income, demographics, and forms of government within countries.

Because both terrorism and the low social status of women in the Middle East are often

scrutinized, it makes sense to determine whether there is a link between the two other

than the prevalence of Islam. Studies on the interaction between womens education and

national security include hypotheses that womens education may affect their intake of

information from government, religious, or other sources; change the way women raise

their children; or alter political trends within a country by increasing womens political

participation.

While many of the larger effects of womens education on society are difficult to

quantify, one measurable impact is the effect of womens education on fertility. Studies

have shown that an increase in womens education reduces individual and aggregate

fertility rates, which affects the age structure of a countrys population. One strongly

held belief about the determinants of terrorism is that having a large proportion of young

males in the population increases the likelihood of terrorism. ANewsweek article

published shortly after 9/11 by Fareed Zakaria proposes the idea that the existing

demographic structure of the Middle East may have contributed to the recent wave of

terrorism:

A huge influx of restless young men in any country is bad news


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In this paper, I show empirically that while womens education does affect

population age structures, the resulting demographic change does not significantly affect

terrorism. First, I show that an increase in womens education in developing countries

results in a decline in fertility. Since countries with high fertility rates generally have a

large proportion of the population in the younger age groups, this reduction in the fertility

rate changes the demographic shape of a countrys population over time towards having

fewer young people.

This demographic shift could reduce terrorism through multiple channels. By

extending the economic model of an individuals decision to participate in criminal

activities to model the terrorism participation decision, we can see that young males are

the most likely demographic to participate in terrorist activity. Furthermore, it is possible

that there are aggregate effects such that having more young males in society increases

the frustration felt by young males seeking jobs and mates. With fewer young males in a

country, the pool of frustrated young men from which terrorist organizations can recruit

is reduced, potentially leading to lower levels of terrorism.

Contrary to these hypotheses, my results show that after an increase in womens

education has reduced the proportion of young males in society, there is no reduction in

terrorism. Further analysis of the relationship between terrorism and the native young

male share of the population (the young male population excluding immigrants) shows

that a decrease in the native young male share also does not decrease terrorism.


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Caribbean, Melanesia, Micronesia, and Polynesia. The second set of data includes a

broad definition of the Middle East/North Africa region (MENA), ranging from Algeria

to Pakistan and including many of the Central Asian countries. Tables 10 through 13

include additional results for the more developed countries and for smaller subsets of data

organized by continent. Table 14 lists the countries that are included in each of these

data sets.

My research contributes to the terrorism literature by examining both individual

motivations for terrorism and societal factors that may make a country more likely to be

affected by terrorism. I also contribute to the literature on womens education and

demographics by estimating the effect of womens education on terrorism through this

demographic channel. While my results challenge the hypothesis that womens

education will reduce terrorism by changing demographic structure, it is still true that

womens education is beneficial for development in general. Furthermore, the effect of

womens education on other aspects of society, including politics and civil society, are

less quantifiable but warrant further study.

I begin this paper by reviewing the literature on women and terrorism in order to

identify the channels through which womens status may affect terrorism. I will then

develop the theory behind my particular identification strategy. Next, I will explain the

empirical strategies used to identify the steps of the causal chain and the data used to test

these theories. Finally, I include separate results for the developing countries and the


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2. Literature Review

Before 9/11, the study of terrorism was limited to a few political scientists and

even fewer economists. When the attack on the World Trade Centers spurred popular

interest in explaining and preventing terrorism, there was new attention given to the

regimes under which terrorist networks thrived. With this attention came a renewed

scrutiny of the lack of political and economic freedom for citizens and especially for

women under these regimes. In parallel to popular interest, academic study of terrorism

has also grown. Recent research has looked at the general correlates of terrorism, the

relationship between religious and political freedoms and terrorism, and the effect of

economic variables such as education and poverty on the likelihood of terrorist events.

Quan Li and Drew Shaub (2004) have found that transnational terrorism is more likely to

occur in countries with lower per-capita GDP. Simon Haddad and Hilal Khashan (2002)

have shown that countries with a greater prevalence of political Islam generally show

stronger support for terrorism. These studies paint a picture of terrorism growing under

developing countries with Islamic regimes. Since these countries are often the same ones

that are criticized for allowing or even promoting gender inequality, it would be useful to

examine the possibility of a direct link between terrorism and the generally inferior

economic, social, and political status of women in developing countries around the world.

In general, the literature has been inconclusive on the overall effect of womens

education on terrorism mostly because of the difficulty in isolating the effect of an


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status would allow for more peace efforts. However, others argue that aggregate levels of

education are unrelated to terrorism. One researcher even argues that womens education

in particular may actually increase terrorism because educated women will be more likely

to feel frustrated with the existing social or political atmosphere, increasing their

likelihood of supporting terrorism as a means for change. Because these effects are

difficult to uncover and quantify, I extend the literature by examining the measurable

impact of womens education on terrorism through the effect of education on fertility and

population demographics.

Research on the relationship between women and terrorism has addressed a

variety of channels through which education can affect womens beliefs, actions, and

influence on society. A number of these studies rely on the characterization of women as

members of society who are generally less aggressive and who prefer peaceful solutions

to conflicts. Using this premise, some studies have shown that increased womens

education or social status can result in increased political or nongovernmental

organization involvement focused on promoting peace. In a report published by the

United Nations Development Fund for Women, Elisabeth Rehn and Ellen Johnson Sirleaf

(2002) document womens participation in peace efforts worldwide and effectiveness in

resolving conflict. In countries that suppress public action by women, efforts by women

to promote peace may be thwarted. Furthermore, we might expect that male-dominated

societies are more likely to experience conflict and violence. Mary Caprioli (2000)


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decision-making. The study relates the likelihood of militarized interstate disputes to

general womens outcomes, including duration of female suffrage and percentage of

women in parliament. Even after controlling for levels of wealth and democracy, having

more women involved in the political process results in a lower likelihood of a state using

force in international conflicts.

Since the study of women and conflict has generally supported the idea that better

womens outcomes will reduce the use of violence, it makes sense that the same

relationship would hold true for terrorism. However, there have been two recent

challenges to the assumption that better education and less poverty for women will result

in less terrorism. Amy Caiazza (2001) points out that educated women are more likely to

become frustrated with the economic and political constraints of their political regimes.

If this frustration grows sufficiently, educated women may feel confined by political

regimes enough to begin supporting terrorism. In the past, women have committed large

and small terrorist acts ranging from the high-profile suicide bombing that resulted in the

assassination of Indian Prime Minister Rajiv Gandhi in 1991 to lower-profile

participation of women in terrorist networks. Another avenue available to women who

face limits on their public activism is to raise families committed to militaristic or

terrorist causes and encourage their sons and husbands to engage in terrorist activity.

Unlike in the earlier case of decision-making in interstate disputes, these examples

suggest that increasing womens education without simultaneously improving domestic


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terrorism. Alan B. Krueger and Jitka Maleckova (2003) do not focus on gender

differences but use data from Israel and Palestine to show that educational attainment and

income level do not affect the likelihood that a person will become a terrorist or support

terrorist action. Since this study concentrates on individual effects, it may be the case

that improvement in average income and aggregate education reduces terrorism. To

examine this possibility, these same researchers run preliminary tests on a cross-country

data set and tentatively assert that after controlling for civil liberties, the relative wealth

and literacy rate of a country are not good predictors of terrorist events that take place

within the country. Despite these empirical findings, the authors acknowledge that more

cross-country comparison is needed, especially given the negative relationship between

economic conditions and civil conflict, and the positive relationship between civil

conflict and terrorism. Haddad and Khashan (2002) also use a sample of politically well-

informed people in Lebanon to examine opinions on the 9/11 attacks, and find no

significant effect of income or education upon responses.

The theories that have been posited so far provide a number of effects of womens

education that could result in an overall positive or negative effect on terrorism. Since it

is possible to measure womens involvement in the public arena of political decision-

making, Caprioli (2000) is able to measure the effect of womens political participation

on the use of violence in international disputes. Discerning the impact of women on

terrorist networks is more difficult because the veiled nature of terrorist networks


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measures of womens status that have not yet been studied in the context of terrorism are

womens educational attainment, literacy rates, and labor force participation. However,

since these measures are often strongly correlated with other country characteristics that

are also associated with terrorism, including income and civil liberties, it is more useful

to study a mechanism of womens influence that is unrelated to these factors.

Many of the competing effects of womens education on terrorism discussed

above are obscured by the secrecy surrounding terrorist networks. Measuring this effect

is further complicated by the relationship of womens education to civil liberties, income,

and other variables that may affect terrorism. However, one measurable mechanism

through which women may affect terrorism is through their role in shaping the

demographic profile of a country. Fertility is a measurable variable that is closely linked

to womens rights and education. In the next section, I explain one causal chain that links

womens education to terrorism. I begin by linking an increase in womens education to

a reduction in fertility and go on to argue that a lower fertility rate today will later result

in a smaller proportion of the population being in the critical age range of fifteen to

twenty-four years old. To relate the young male share of the population to terrorism, I

first apply an economic model of the decision to participate in criminal activity to

terrorism to show that young males are the demographic most likely to engage in

terrorism. Next, I examine the possible aggregate effects of the young male share on

terrorism. Finally, I develop an extension of the model relating fertility rates specifically


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3. Economic Theory

Increased Education Reduces Fertility

To examine the effect that womens education has on terrorism, I first look at the

effect of womens education on fertility. In this study, I use the World Banks database

of World Development Indicators, which defines fertility as the number of children that

would be born to a woman if she were to live to the end of her childbearing years and

bear children in accordance with prevailing age-specific fertility rates (World Bank,

World Development Indicators 2004). There are a number of reasons why we would

expect womens education and fertility to be negatively correlated. First, educated

women have better access to contraceptive information, whether it comes in the form of

general media information about fertility or from direct efforts by fertility reduction

campaigns. Second, as Frances Vavrus and Ulla Larsen (2003) have shown, educated

women use contraceptives more often and more effectively than uneducated women do.

Third, educated women are more likely to delay marriage, which reduces the number of

childbearing years in a womans lifetime. Fourth, as educated women enter the

workforce, they face a higher opportunity cost of childbearing. This opportunity cost can

be measured as the foregone salary or career advancement opportunities that come with

pregnancy, or less tangibly as the foregone self-fulfillment that women gain from a

productive career. Fifth, womens education can have a positive effect on the social

status of women within the immediate or extended family that increases their control over


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for children that increases the cost of childrearing, and the lower likelihood of reliance on

children as a source of support in old age (ystein Kravdal, 2002).

In comparing aggregate levels of education and fertility across countries, we

might suspect that a negative correlation between womens education levels and fertility

indicates a more general trend of development, which raises both the level of human

capital and the average age of the population. However, Vavrus and Larsen (2003) have

used micro-level data in Tanzania and Uganda to demonstrate that there are negative

effects of education upon womens fertility decisions at the individual level. Kravdal

(2002) has gone a step further by using demographic and health surveys for twenty-two

countries to distinguish the effect of individual education from that of aggregate levels of

education on individual fertility decisions. Another concern we would have in

identifying the effect of womens education on fertility would be that the education only

reduces fertility through its effect on womens employment, which would imply that an

increase in education would not reduce fertility in absence of labor market opportunities.

If education only reduced fertility through a change in labor market opportunities, we

would expect that uneducated women would have similar fertility rates in any country,

and that the fertility rate for educated women would depend upon domestic labor market

opportunities. In other words, we would expect similar fertility rates across countries for

uneducated women and a divergence in fertility rates for educated women, depending on

labor market opportunity. However, Anrudh Jain (1981) shows that fertility levels of


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Lower Fertility Reduces the Number of Young Males in Society

In order to measure the effect of womens education on terrorism, I take the

negative relationship between womens education and fertility and examine its effect on

the next variable in the model: the number of young males in society as a percentage of

the population. The link between fertility and the number of young males in society is

relatively obvious when we consider fertility as a statistic that is highly correlated with

population growth and the young male percentage as a statistic that reflects the age

structure of a population. By definition, a high current fertility rate reflects the relatively

large number of births. As these babies grow to become adults age 15-24, it makes sense

that their cohort would be larger if the fertility rate at the time of their birth were higher.

Because my theory rests on the prevalence of young males in society, attitudes

about gender may affect the relationship between fertility and young male population

share. In countries where male children are preferred to females, we would expect to see

the effect of a high fertility rate magnified by the effect of actions based upon the

preference for males, such as female infanticide. As these preferences have their largest

effects when a child is very young, we would expect attitudes at the time of birth to have

the largest effect on the later young male population, rather than the attitudes about

gender at the time the population bulge reaches young adulthood. Inclusion of sex ratio

data concurrent with fertility data improves the estimation of the relationship between

fertility rates and the young male share of the population by controlling for the effect of


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population is a better measurement of gender preferences than the sex ratio of the entire

population, because it is less affected by migration.

Another control specific to the relationship between fertility and young male

share is infant mortality. We would expect that women in countries with higher infant

mortality would give birth to more children over their lifetime than the number of

children they eventually want to raise, in order to compensate for the high mortality rates.

In order to better relate fertility to the young male share of the population, I include data

from the World Development Indicators on infant mortality as a control.

Young Males and Terrorism: Individual Effects

The final step in the causal chain linking womens education to terrorism is the

relationship between young male share and terrorism. The hypothesis is that a higher

percentage of the population in the male, 15-24 year-old demographic range will cause an

increase in terrorism. By applying the rational choice model, theory predicts that young

males are the most likely demographic to participate in terrorist activities.2 Given this

prediction, it makes sense that having more young males in a country means that more

people will choose to participate in terrorist activities, resulting in higher levels of

terrorism. However, an analysis of the effect of an aggregate increase in young males

offers alternative predictions for the effect of an increase in young males, including the

possibility that since the total number of terrorists is so small relative to the entire


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Just as younger males accrue more indirect financial benefit because they will live under

the new regime for a longer period of time, they will also derive more psychic benefit

from political change because they will live under a more favorable political regime for a

longer period of time.

Unemployment is another cause of frustration in young males that can lead to

terrorism. Unemployment already has a direct effect on the cost of terrorism by lowering

the opportunity cost for young males. In addition, high unemployment can make it

difficult for young males to gain independence, status, and livelihood (Richard P.

Cincotta, Robert Engleman, and Danielle Anastasion, 2003). As a result, young males

may become disaffected with the political environment, blaming their frustration upon

domestic or outside forces that seem to contribute to the unsatisfactory economic

conditions. Since unemployment is much higher for young males than it is for older

males, we would expect the frustration and therefore psychic benefit from action taken to

be higher for young males than for older males.

In summary, theory does not point to a greater benefit for younger or older males

from terrorist actions. However, because theory does predict a lower relative cost for

younger males, we would still expect to see that the benefits outweigh the costs for

younger males more often than for older males. Therefore, we would expect that younger

males would be more likely to participate in terrorism.

Recent literature includes ample evidence linking the young male demographic to


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the 1992 Los Angeles riots were males between the ages of sixteen and thirty, a

demographic that was subject to high unemployment and low homeownership rates at the

time. These cases fit the cost-benefit analysis above and are examples of cases in which

young males with a low opportunity cost of time and low societal responsibilities engage

in violent or criminal activity.

Though evidence to support this model for terrorism is less common, some recent

studies contain anecdotal evidence linking young males to terrorism. In Krueger and

Maleckovas (2003) analysis of Hezbollah fighters, 85% percent of the Hezbollah

fighters who died were between fifteen and twenty-five years old, while only 20% of the

entire population of Lebanon was in this age range. In another case study, Fernando

Reinares (2004) uses judicial proceedings to determine the demographic profile of

militants recruited by ETA (Euskadi ta Askatasuna or Basque Homeland and Freedom).

Using data on nearly half of all ETA recruits between 1970 and 1995, he finds that 66.1%

of militants were recruited when they were between the ages of 18 and 23, with an

additional 18.2% recruited between the ages of 24 and 26. These studies are

encouraging, but are limited to specific regions with long histories of terrorism. A cross-

country comparison could help discern whether the relationship between the age

distribution of a population and terrorism is more widespread.

Young Males and Terrorism: Aggregate Effects


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that an increase in the young male share will not increase terrorism. We would expect to

see this result if the number of terrorists is so small as compared to the population that

terrorist organizations have as many members as they desire. In other words, if terrorist

organizations can control the size of their membership and have enough members, then

having more young males in the population may not affect the size of the terrorist

network. A second possible relationship between young male share and terrorism is that

terrorism will increase proportionally with the young male share. We would expect to

see this result if there is a constant likelihood of an individual young male becoming a

terrorist. As the number of young males increases, the number of terrorists increases at

the same rate.

A third possibility is that an increase in the number of young males will result in a

greater than proportional increase in terrorists. This scenario is possible if there is a

strong component of competition between young males for jobs or mates, so that having

more young males increases competition and frustration in society. For example, in the

model of individual decision-making above, unemployment affects the opportunity cost

of participating in terrorism. It is possible that an increase in the number of young males

will more than proportionally increase unemployment for this demographic, which could

have large effects on the number of individuals who decide to participate in terrorism.

Another factor that could result in a more-than-proportional increase in terrorism is the

competition for mates. In societies with a large percentage of young males, an increase


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immigrant young males. If this is true, then it makes sense to study the native young

male share as the most relevant population to terrorism. Immigrants often leave their

home countries for employment opportunity, and return to their home countries

voluntarily or forcibly when the employment situation is poor. Since immigrants are

more likely to be employed than native young males, they also have a higher opportunity

cost of participation in terrorist activities. Finally, since immigrants choose their

destinations, they are less likely to be politically disaffected in their destination countries.

While this model is motivated by the belief that immigrants are not likely to

participate in terrorism, one strength of the model is that it still holds if the converse is

true. Any terrorist incident committed by an immigrant outside of his home country is

attributed to the nationality of the terrorist, or his home country. In this way, the terrorist

event is causally related to the womens education and demographic characteristics of his

home country. This model draws a parallel between the motivations for emigration,

including opportunity cost and societal frustration, and the similar motivations for

terrorism.

4. Methods

To examine the relationship between women and terrorism, I will look at womens

role in shaping the demographic profile of a country and the relationship between the

demographic profile and the prevalence of terrorism In all regressions observations are


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The first regression relates womens education, measured by adult female literacy,

to the fertility rate. Because many of the effects of womens education on fertility only

materialize in the presence of opportunities for female labor market participation, I have

included the percentage of the labor force that is female as a control. By including this

control, I have ensured that the estimation of 1 as the effect of education on fertility is

valid even in the absence of these labor market opportunities. My regression also

includes variables for the level and growth of per capita GDP to control for the effects of

macroeconomic conditions on individual fertility decisions, and a variable for infant

mortality to control for the possibility that high fertility is due to compensation for high

mortality rates. Finally, in order to prevent an omitted variable bias resulting from the

effect of religion upon female education and fertility, I also include a control variable that

measures the percentage of the population that is Muslim. In the following OLS

regression, 1is the variable of interest, because it measures the effect of adult female

literacy on fertility.

Regression 1:

Fertilityit= 0+ 1Adult Female Li teracyit+ 2Female Labor Forceit+ 3Infant Mortalityit+ 4GDP per capitait + 5GDP per capita growthit+ 6Muslim shareit +i + t +uit

My second regression relates fertility to the young male share of the population.

In this step, I regress the young male share of the population on a value of fertility that is

lagged twenty years. In order to improve the estimation of the effect of fertility in this


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regression estimation are used to predict values for the native young male share. The

second stage uses Regression 3 to estimate the effect of this predicted value of native

young male share on terrorism. This instrumented approach is required because detailed

data on migration is scarce, making it difficult to use the actual values for native young

male share. By using fertility as an instrument, I ensure that the size of the predicted

young male population is unaffected by migration.

In order for fertility to be a valid instrument, it must be correlated with the youth

male share of the population and exogenous with respect to terrorism. The correlation

between lagged fertility and the native young male share of the population is obvious by

examination of the general course of birth and aging that relates higher fertility to higher

population shares. However, the justification for the independence criterion requires

some explanation. Fertility, as I have shown, is strongly associated with education. As

such, we can guess that it is also strongly associated with wealth. If education and wealth

are negatively correlated with terrorism at the individual or national level, then we might

suspect that using fertility as an instrument for the native youth male share of the

population would introduce bias from the omitted variables of education and wealth.

However, I believe that individual and aggregate effects of education and wealth are

factors that are either unrelated to terrorism or can be controlled for in the regressions.

With respect to individual effects, there is evidence that if wealth and education are

correlated to terrorism at all, the relationship is a positive one. As noted above, Krueger


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correlations are much weaker with respect to violent crimes. Since terrorism is more like

violent crime in that the motivation is rarely financial gain, we would expect terrorism

also to have little to no correlation with poverty and education. Krueger and Maleckova

further argue that educated people are more likely to become terrorists because they are

more likely to have the feelings of indignity and frustration that motivate terrorism and

because they have skills that would make them more suitable choices for terrorist

networks. Krueger and Maleckovas study supports these hypotheses by showing that

Hezbollah fighters are generally wealthier and better educated than the Lebanese

population. In addition, they cite surveys that show stronger support for terrorism from

better-educated respondents. Thus, individual wealth and education are either positively

associated with terrorism (which would bias my estimates downward), or are

unassociated with terrorism, leaving fertility as a sound instrument.

Though individual wealth and education can be accounted for in this way, societal

factors are more complex. To examine the societal factors of terrorism, I take Krueger

and Maleckovas assumption that terrorism is a response to political conditions and

long-standing feelings of indignity and frustration. With this characterization, we would

expect to see relatively wealthy and well-educated terrorists motivated by political factors

that may be associated with lower overall levels of wealth and education. I control for

the effect of societal wealth by including current levels of per capita GDP. The low

aggregate levels of education in such a society are related to terrorism only to the extent


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the terrorist event. In contrast, the level of education that affects the instrumental

variable is a measure of conditions about thirty years prior. This lag is composed of two

parts: first a lag between the education of a woman and her childbearing years (about ten

years) and then the lag between the birth of a child and his reaching the critical age in the

this study (about twenty years). We can say that current and lagged education variables

are certainly correlated. However, if we take terrorism as an event with motivations

mostly based on the political environment at the time of the attack, it is not unreasonable

to assume that the education level from thirty years ago which affects the fertility rate

from twenty years ago is unrelated to the incidence of terrorism today.

Given this justification, an instrumental variables approach using a lagged value

for fertility can be used to measure the effect of native youth male share on terrorism.

Infant mortality and sex ratio are used as controls in order to improve the prediction of

the native young male share. Economic controls include the level of GDP per capita;

social controls include the literacy rate, Muslim share of the population, and ethno-

linguistic fractionalization; and political controls include measures of political rights and

civil liberties. Each of these is discussed in the data description that follows. The two

stages are as follows:

Regression 4, First Stage:Young Malesit= 0+ 1Fertilityi(t-20) + 2 Sex Ratioi(t-20) + 3Infant Mortalityi(t-20)+4GDP per capitait + 5Literacyit + 6Political rightsit+ 7Civil libertiesit+

8Muslim shareit + 9Ethno-linguistic fractionalizationi + i + t+ eit


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5. Data Description

Education

Data on education is used in each of the four regressions. In each case, I have

used measures of literacy for the relevant population from the World Banks World

Development Indicators (WDI) database. The World Bank definition of adult literacy is

the percentage of people ages 15 and above who can, with understanding, read and write

a short, simple statement on their everyday life.

There are a number of reasonable measures of education that would be relevant to

the regression of fertility on education, including primary enrollment, secondary

enrollment, educational attainment, and literacy. The best data for each of these

measures would be limited to describing women of childbearing age, generally defined as

ages 15 to 44. However, data on women restricted to this age range is sparse. Since

measures for adult females, ages 15 and over, are a close correlate and are widely

available, I have used data for this age range.

The various measures of womens education all relate to womens status and

knowledge attained. An increase in any of the education variables should indicate that

women are better able to use information provided and make better-informed choices.

Measures of enrollment and attainment are more closely related to womens status,

including decisions about marriage, childbearing, and labor participation. Literacy

captures some of this but also describes the result of womens education or the actual


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unrepresentative of general trends. However, I have chosen to use literacy because it best

captures female knowledge and has the greatest country and year coverage. Adult female

literacy data from the WDI is available from 1970 to 2003 on an annual basis. Female

literacy in developing countries has grown steadily in over this period from 43.2% in

1970 to 72.6% in 2000. While the WDI reports literacy rates as high as 99% for some

countries, others are listed only as estimated to be greater than 95%. To homogenize

the data, I have top coded adult female literacy at 95%.

Fertility

My measurement for fertility also comes from the World Development Indicators

database of the World Bank, which compiles data from census reports, the UN Statistical

Division, country statistical offices, and Demographic and Health Surveys to measure

fertility. It defines fertility as the number of children that would be born to a woman if

she were to live to the end of her childbearing years and bear children in accordance with

prevailing age-specific fertility rates. The data are available for about 180 countries at

least every two to three years from 1960 to 2002. Because the data is available at regular

intervals, I have linearly imputed the fertility for years with missing values. During this

time period, there was a fall in the average fertility in developing countries from 5.05 to

2.76. Though the general trend of declining fertility applies globally, a few countries

have experienced increases in fertility, and a number of countries have experienced very


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data from 1960 to 1980 are used as lagged variables, such that the value of the young

male share in 1985 for example, is regressed on the value of fertility in 1965. These data

have a similar range and a slightly higher mean of 6.01 due to the global trend of

reduction in fertility over the last four decades.

Young Male Percentage

The young male share variable used in Regressions 2 and 3 is the number of

males ages 15 to 24 as a percent of the population for a given country and year period.

These data are available in the World Population Prospects database compiled by the

United Nations by country and region on an annual basis between 1950 and 2000. Table

1 shows the percentage of young males in major regions of the world in ten-year periods

from 1950 to 2000. The percentage of the population in this young male category for

major regions varies between 13.6% for North America in the 1950s to 20.6% for Africa

in 2000. Even with this variation, there is a general trend of falling percentages of young

males in the population from 1950 to the mid-1960s, a rise from the mid-1960s until

around 1980, and a return to lower percentages throughout the 1980s and 1990s. One

major exception to this trend is within Africa, where the percentage has increased steadily

since 1964. The variation among individual countries and trends should be helpful,

because it will provide the variation that can be used to explain terrorism in countries

over time. In addition, as trends continue, we will be able to anticipate their effects on


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terrorist group that committed the action if known. While it is easy to attribute each

event to a particular year, relating each event to a particular country is more difficult, as

there can be up to three countries associated with each event.

One obvious choice of country would be the location of the event. If we believe

that a large proportion of males in society makes the society generally more unstable,

then this attribution would be the best choice. Having more males may affect general law

enforcement and security, leaving vulnerabilities that terrorists may take advantage of

while planning locations of attacks. In addition, if we assume that young males are likely

to be the terrorist and that terrorists generally attack in their home country, then the

location of the attack would be the right country attribution. However, because this

database covers only international terrorist events, the attackers home country is the

same as the events location in only about half of the events for which nationality of the

attacker is known. Another choice for country attribution would be the intended target of

the attack. While this country is usually the same as the one in which the event occurs,

there are instances in which terrorism against one country occurs elsewhere. For

example, the embassy bombings of 1998 occurred in Kenya and Tanzania occurred in

Africa, but were clearly targeted at the United States and were committed by men from

Egypt, Kenya, Lebanon, Libya, and Saudi Arabia, among others. In cases like this one,

the number of young males in the target country does not seem closely related to the

terrorist event. The best choice for country attribution then, is the home county of the


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home country. Therefore, each incident should be attributed to the country of the

attacker. The drawback to this attribution is that for about one-third of the events in the

data set, the nationality of the attacker is unavailable. For the 8,000 remaining

observations, up to three nationalities are listed for the terrorists. I have attributed each

event to each country that supplies an attacker, counting each event up to three times.

After attributing country and year to each terrorist event, the final measurement

choice is the effect of the terrorist event. I have used two dependent variables in my

terrorism regressions: incident counts and casualties. Each observation is identified

uniquely by country and year. The incident count measurement is the number of events

attributed to a given country and year pair by terrorist nationality. In order to smooth the

data, the observation for each country year pair is aggregated into five-year periods, so

that the incident count for a given country and year includes events from two years prior

and two years following the year of the observation. For developing countries, this

variable has a mean of 7.56, and ranges from 0 to 169. For the Middle East/North Africa

(MENA) subset of the data, the mean is 13.12, and the range is 0 to 156. The casualties

measurement is a sum of all the injuries and deaths related to every terrorist event

attributed to a given country and year by terrorist nationality. This variable is aggregated

into five-year sums in the same way as the incident count variable. The range of this

variable is 0 to 1098 for both the developing countries data set and the MENA subset.

However, the mean for developing countries is 29, while the mean for MENA is 62.


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Control Variables

Regression 1, which estimates the effect of womens education on fertility,

includes controls for womens labor participation, infant mortality, GDP per capita, GDP

per capita growth, and Muslim share. The data source for the percentage of the labor

force that is female is the World Banks World Development Indicators (WDI), which

uses measurements from the International Labour Organization to show the extent to

which women are active in the labor force. According to the WDI, the labor force

comprises all people who meet the International Labour Organization's definition of the

economically active population. For the observations used in the fertility regression for

developing countries, this variable ranges from 5.1 to 52.5 percent with a mean of 35.9.

The MENA region has a similar range and a much lower mean of 27.1 percent.

Infant mortality data was drawn from the same source as the young male share

data, the World Population Prospects database by the United Nations. The data is

available in five-year increments from 1950 to 2000, and was linearly imputed for the

intervening years. GDP per capita levels and growth rates were drawn from the World

Banks World Development Indicators database, which includes data from 1960 to 2003

in thousands of constant 1995 US Dollars. Finally, the control for religion measures the

percentage of the population identified as Muslim. The 2004 data for this variable comes

from the CIA 2004 World Factbook. Earlier values were taken from the World Christian

Encyclopedia by Barrett, Kurian, and Johnson, which lists Muslim percentage in five-


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mean for the developing countries is around 30 percent, while the mean for the MENA

subset is around 80 percent.

Regression 2, which estimates the effect of lagged fertility on the young male

share, includes four control variables, two of which are lagged. The lagged variables are

sex ratio and infant mortality. With this lag, the infant mortality and sex ratio in 1965

serve as controls for the young male share in 1985. Data for both of these variables

comes from the United Nations World Population Prospects database. The sex ratio was

computed by dividing the number of males in the 0 to 15 year old age range by the

number of females in this same age range. The infant mortality data is a lagged value of

the same data used in Regression 1. The other two controls used in Regression 2 are

measures of GDP per capita and literacy. GDP data is the same data used in Regression 1

from the World Development Indicators. Literacy data also comes from the World

Development Indicators and is similar to the independent variable data from Regression

1. However, instead of using adult female literacy, I have used total adult literacy as the

control in Regressions 2, 3, and 4. According to the WDI, world literacy in developing

countries has increased from 53.5% in 1970 to 78.4% in 2000. Just as with adult female

literacy, because some countries literacy data is estimated at over 95%, I have top

coded total adult literacy data at 95%.

The final set of controls added to Regressions 3 and 4 includes political rights,

civil liberties, Muslim share, and ethno-linguistic fractionalization. Political rights and


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representing the highest degree of freedom. The survey of political rights includes

measurements of the electoral process, political pluralism, and functioning of

government. Civil liberties are measured by freedom of expression or belief,

associational and organizational rights, rule of law, and personal autonomy and

individual rights.

Muslim share data used in Regressions 3 and 4 is the same as the data used in

Regression 1. Finally, the variable for ethno-linguistic fractionalization serves as a

control for diversity within the domestic population. This data was obtained from a data

set compiled by Professor Philip Roeder of the University of California at San Diego.

This variable is measured as one minus the Herfindahl index, which compares the size of

each ethno-linguistic group to the entire population. The variable ranges from 0 to 1 and

increases with greater diversity in the population. The mean of the variable is 0.50 for

the developing countries data set and 0.37 for the MENA subset. Because this variable is

non-time-varying, it is not included in fixed effects regressions.

My results use two subsets of data that are region specific. The first data set uses

all observations from developing countries for which measures of all independent,

dependent, and control variable data is available. The second data set similarly uses

observations from the MENA region for which all data is available. Tables 4 and 5 list

regional means for the main and control variables used in all regressions. In addition,

more detailed summary statistics are listed for the developing countries data set in Tables


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6. Results Developing Countries

Regression 1: Education and Fertility

The results for the relationship between education and fertility support the

hypothesis that an increase in female education will reduce fertility. Looking at Table

A6, we can see that the independent variable, adult female literacy, affects fertility at the

1% level of significance. The coefficient for the independent variable in the fixed effects

regression is -0.041, which seems small. However, examining the summary statistics for

literacy and fertility in Table A4 are enlightening. For the observations used in this

regression, the standard deviation for literacy and fertility are 27.49 and 1.69,

respectively. Taking the summary statistics with the regression estimation, we find that

increasing literacy by one standard deviation would result in a reduction in fertility of 1.1,

which is 0.7 of a standard deviation in fertility.

Because the regression includes controls for economic conditions and religion, we

can assume that the measured effect of womens education is due to the direct effect of

education on fertility decisions and not due to wealth, labor market participation, or

religion. In the fixed effects model, the coefficients on infant mortality and Muslim share

are significant at the 1% and 5% levels, respectively. As expected, fertility rises with

both of these variables. A one standard deviation increase in infant mortality increases

fertility by 0.7 children, or 0.4 of a standard deviation. A one standard deviation increase

in Muslim share increases fertility by 2 4 children or 1 4 standard deviations


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result is demonstrated by both specifications, the coefficient loses significance in the

fixed effects model. In the fixed effects model, the results for GDP per capita are

significant at the 5% level but in the opposite direction that was expected: a rise in wealth

seems to indicate a higher level of fertility. It is possible that the observation of high

fertilities in low-income countries is driven by low education and high infant mortality.

After controlling for each of these factors, an increase in wealth seems to increase

fertility. One possible explanation for this result is that when factors such as education

and infant mortality are held constant, a more wealthy family is able to support more

children. It is important to note that this effect, while significant at the 5% level in the

specification with fixed effects, is not significant in the specification without fixed

effects. In total then, there is only weak evidence that an increase in GDP will increase

fertility.

Regression 2: Fertility and Young Male Share

As expected, the results show that increasing fertility increases the total young

male share twenty years later. Table A7 shows that the magnitude of this effect is large

and significant at the 1% level. According to the results for the fixed effects

specification, a one standard deviation increase in fertility results in a 1.1 standard

deviation increase in the young male share. Many of the control variables are significant

in the first specification (without fixed effects) but lose significance when fixed effects


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share in the fixed effects model. In the model without fixed effects, young male share

increases with sex ratio and decreases with GDP as expected. These results are

significant at the 1% level. Surprisingly, young male share seems to increase with

literacy. While this result is significant at the 1% level, the magnitude of the effect is not

very large. A one standard deviation increase in literacy increases young male share by

0.4 standard deviations. Infant mortality does not seem to affect young male share after

the inclusion of fertility and the other control variables.

Regression 3: Young Male Share and Terrorism

The results from Regression 3, summarized in Table A8, provide evidence that an

increase in the young male share does not increase terrorism, as measured by incident

counts or casualties. The four specifications for this regression include two options for

the dependent variable (incident counts and casualties) and two options for fixed effects

(none or year and country fixed effects). The only specification with a significant result

for the coefficient on young males is specification 3, which has a negative coefficient.

These regressions do not provide evidence for or against the theory that young males are

the most likely demographic to engage in terrorist actions. However, they do provide

evidence to support one of the theories that links the number of young males in the

population to terrorism. The data suggests that an increasing share of young males does

not result in a constant or increasing likelihood of a young male becoming a terrorist.


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As in the results for Regression 2, many of the controls lose significance when

fixed effects are added, likely because there is not enough variation in these variables to

estimate their effects. However, by comparing the specifications with and without fixed

effects, we can get a sense of how terrorism is affected by each of these variables.

Because the distribution of the terrorism variables is skewed, it makes sense to compare

the effects of the control variables in absolute numbers of incidents and casualties instead

of standard deviations.

According to the fixed effects models, a one standard deviation increase in GDP

results in about 2.9 fewer terrorist events and about 26 fewer casualties over a five-year

period. These results weakly suggest that when the affected countries are limited to those

with low incomes to begin with, as in this developing country data set, an increase in

wealth will reduce terrorism. This finding supports Krueger and Maleckovas hypothesis

that long-standing inequalities or poverty may encourage terrorism. However, it is clear

just by looking at Table 3, which lists the countries producing the most terrorists, that

terrorism affects countries in every income group. Therefore, these results should not be

extrapolated to countries with higher levels of societal wealth. Further results for

Regression 3 applied to different regional and income level subsets of countries can be

found in Tables 10 and 11. While these coefficient estimates are not significant, the

regressions weakly suggest that an increase in wealth may reduce the number of terrorist

incidents across the developing world but not in the more developed countries.


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literacy rates produce more terrorists. Though the coefficients on literacy are not well

estimated in the fixed effects regressions, they suggest that a one standard deviation

increase in literacy results in about 7 additional incidents and about 92 additional

casualties.

In examining political rights and civil liberties, the overall effect of a

simultaneous decrease in political rights and civil liberties is to increase terrorism. In the

regressions without fixed effects, the magnitudes of these coefficients are particularly

large and significant, especially considering that the scale of these variables is from 1 to

7. As with earlier control variables, the significance is dropped when fixed effects are

added to the incident count regression. However, since the significance is maintained in

the casualties regression, and the signs are constant throughout the four regressions, it is

still useful to interpret the implications of these coefficients.

The regressions show effects of similar magnitudes but in opposite directions for

political rights and civil liberties. The similar, large magnitudes may be explained by the

high correlation between these two variables for the observations used in this regression

estimation (R2= 0.887). It is important to note that these variables are measured such

that an increase in value reflects a decrease in rights. When both political rights and civil

liberties increase by one, the relative degree of freedom falls, and the total effect is to

increase terrorism by between 0.2 and 0.6 incidents and between 5 and 7 casualties over

five years (with and without fixed effects, respectively). Therefore, countries with fewer


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total effect on terrorism. While the collinearity of political rights and civil liberties is of

some concern, the coefficients for the independent variable and all other control variables

maintain similar magnitudes and levels of significance in specifications that omit either

one of these two variables.

While the correlation between the political rights and civil liberties variables is

high, an analysis of the separate effects of each variable can explain why the regression

implies that terrorism increases with political rights and decreases with civil rights. If we

interpret a terrorist action as a statement of political beliefs intended to cause a change, it

makes sense that an increase in civil liberties would open other channels of expression

that would reduce reliance on terrorism as a form of expression. However, it is surprising

that an increase in political rights would increase terrorism. One possible explanation

might be that terrorism is committed by people who find political rights insufficient. The

measurement of political rights in the Freedom House data set uses a broad definition that

evaluates the electoral process, pluralism, and the functioning of government. Because

minority rights comprise a relatively small part of the calculation of this variable, a

general increase in political rights may reduce the power of people in the ethnic,

religious, or linguistic minority, resulting in an increase in terrorism.

Surprisingly, the results only offer weak support that terrorism increases with

Muslim share or ethno-linguistic fractionalization. The coefficients on Muslim share are

not precisely estimated even in the specifications without fixed effects, suggesting that


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no clear relationship between GDP and terrorism, and positive but not significant

coefficients on literacy, Muslim share, and ethno-linguistic fractionalization. In addition,

the coefficients on political rights and civil liberties in each specification of Regression 4

are very similar to the corresponding specification of Regression 3. These results also

support the arguments above, that a simultaneous increase in political rights and civil

liberties reduces terrorism, that an increase in political rights alone will increase

terrorism, and that an increase in civil liberties alone will reduce terrorism.

7. Results Middle East and North Africa

Regression 1: Education and Fertility

The differences between the analysis of womens education and terrorism in the

developing countries and the MENA subset are apparent beginning with the first causal

step, the regression of fertility on education. At the outset, the results for the MENA

region in Table B6 look very different from the results for the developing countries data

set in Table A6. Unlike the developing country regressions, which show a strongly

significant negative effect of education on fertility, these regressions show coefficients of

similar magnitudes but without significance, which only offer weak support that womens

education will reduce fertility in the MENA region. Using the summary statistics found

in Tables B4 and B5 and the results for the fixed effects regression in Table B6, I find

that a one standard deviation increase in adult female literacy in this region results in a


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the 10% level in regressions with and without fixed effects. It is possible that the lack of

significance of this estimation comes from the smaller sample size available for the

MENA region. To begin with, there are fewer countries in the MENA region than in the

set of all developing countries. This data set is further restricted due to missing data from

a number of countries. However, it is also possible that there are region-specific reasons

why education does not reduce fertility in the Middle East. One possible explanation is

that increasing womens education does not increase womens control over fertility

decisions in this region, a change that is thought to negatively affect fertility.

The coefficient estimations for the controls in this regression are similar to the

developing country estimation. One important detail is that the controls for infant

mortality, GDP per capita, and Muslim share are less significant in the fixed effects

model for the MENA region than they were in the developing countries estimation. The

coefficient on infant mortality increases in magnitude but loses some significance,

providing support that a one standard deviation reduction in infant mortality would

reduce fertility by about one child, or 0.61 standard deviations. Similarly, the effect of a

one standard deviation increase in GDP per capita still increases fertility in the fixed

effects model for MENA, but loses significance as compared to the developing countries

estimation. Given the lack of significance of GDP in specifications with and without

fixed effects, and the opposite signs on these coefficients, it seems that there is no

evidence that GDP affects fertility at all in the MENA region. Finally, in the fixed effects


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significant, and so the positive relationship between Muslim share and fertility is only

weakly supported.

Regression 2: Fertility and Young Male Share

As shown in Table B7, the MENA countries experience an increase in the young

male share when fertility increases. However, the estimations of this effect as well as the

effects of almost all control variables do not maintain the significance levels found in the

analysis of developing countries. The developing countries estimation shows a fall in

significance when moving from the model without to the model with fixed effects, likely

because most of the variation in the control variables can be predicted by the values of

the fixed effects. In the MENA regressions, even the model without fixed effects has

only one significant coefficient estimations, which implies either that the causal trends of

the developing countries do not hold in the MENA region, or that the sample size is too

small to make precise estimates of the effects.

While the coefficient on the effect of fertility on young male share is not well

estimated, it is still positive. Because the magnitude of this coefficient is small, the

results imply that in the MENA region, a one standard deviation increase in fertility will

increase the young male share by only 0.44 of a standard deviation. This effect is much

smaller than the one-to-one increase between standard deviations of fertility and young

male share in the developing countries data analysis. One explanation for this difference


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Like fertility, the variables for sex ratio, GDP per capita, and literacy all are

significant in the developing countries analysis (without fixed effects) and not in the

MENA analysis. It is likely that both the effect of immigration and the small sample size

for the MENA region prevent a precise estimation of the effect of these domestic

characteristics on total young male share in MENA. It is surprising that infant mortality,

which was not a good predictor of young male share in the developing countries data set,

is significant in both specifications of the regression for the MENA region. The results

suggest that an increase in infant mortality increases young male share.

Regression 3: Young Male Share and Terrorism

Unlike earlier steps in the causal chain, the analysis of the effect of total young

male share on terrorism in the MENA region is very similar to the analysis for the

developing countries data set. Both sets of results support a negative finding for the

effect of young male share on terrorism. Table B8 shows the regression estimations for

the MENA region, which support the theory that an increase in the young male share

does not increase terrorism as measured by incident counts or casualties. In fact, the

coefficients on young male share for the MENA region are all negative and are larger

than the estimations for the developing countries data set. While these results are not all

well estimated, they certainly present strong evidence that a large young male share does

not increase terrorism. Furthermore, the coefficients on the total young male share in the


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in terrorism. Under this scenario, to study the determinants of terrorism, the variable of

interest should be the native young male share, which is analyzed in Regression 4.

Before moving to the analysis of Regression 4, it is worth noting that the MENA

regressions do not provide support for Krueger and Maleckovas hypothesis that

increasing wealth and education will reduce terrorism, and instead support the argument

that political factors are stronger determinants of terrorism. While it is possible that the

negative findings with respect to education and wealth are caused by the small sample

size of data used in these regressions, it is also possible that the perceived relationship

between these social and economic factors and terrorism is not pervasive, and that

political factors are more important determinants of terrorism. The various specifications

of Regression 3 for the MENA region support an argument for the relative importance of

political factors. They show that increasing political and civil rights is more effective at

reducing terrorism than increasing wealth, and certainly more effective than increasing

education, given the positive coefficient on the literacy variable.

As in the developing countries regressions, political rights and civil liberties are

highly correlated. However, the first specification for the MENA results, which uses

incident counts as the dependent variable and omits the fixed effects, shows that an

increase in both of these variables (a decrease in freedoms) will decrease the number of

incidents. The other three specifications follow the pattern found in the developing

countries analysis: when both political rights and civil liberties increase by one, the


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i id t b t ti ff t f ti l lti Th t d d


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incidents but a negative effect of native young males on casualties. The standard errors

on all four specifications are so large that the coefficients on native young males are not

significant in any of the regressions. Given the opposite signs of these estimates and the

lack of significance, it is reasonable to conclude that the aggregate number of native

young males is unrelated to terrorism.

Just as these regressions do not demonstrate a strong relationship between native

young male share and terrorism, they also do not demonstrate any strong relationships

between the various control variables and terrorism. According to Krueger and

Maleckovas hypothesis, we should see a reduction in terrorism when there is an increase

in education or wealth. In addition, we would expect the MENA region to follow the

general trend of terrorism increasing with Muslim share and ethno-linguistic

fractionalization. However, the standard errors on these variables are very large

compared to the magnitude of the estimated coefficients, leaving almost all coefficient

estimates without any significance. As in the analysis of developing countries, the results

from Regression 4 show no clear relationship between GDP and terrorism. While the

developing countries analysis showed positive coefficients on literacy, Muslim share, and

ethno-linguistic fractionalization, the coefficients on these variables for the MENA

regressions fluctuate in direction, giving no evidence of a relationship between these

variables and terrorism. As with earlier analyses of the MENA region, it is possible that

the sample size is too limited to draw conclusions. However, the fluctuating directions of

are also consistent with the signs in Regression 3 giving further evidence for the earlier


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are also consistent with the signs in Regression 3, giving further evidence for the earlier

argument that terrorism increases with political rights, decreases with civil liberties, and

decreases when both political and civil rights are increased simultaneously.

8. Conclusion

My paper examines one effect of increased womens education upon terrorism.

Terrorism is a phenomenon that afflicts a diverse set of countries, including those that are

rich and poor, advanced and underdeveloped, democratic and lacking freedom. In order

to examine the effect of womens education upon terrorism, I focus on two regions in

which womens education is relatively low to begin with: the developing world and the

Middle East/North Africa region. There may be a number of competing effects of

womens education affecting the characteristics or frequency of terrorism. Because this

web of related factors is difficult to measure, I focus my research on one quantifiable

result of womens education: the effect of education on terrorism through changes in

fertility and country demographics.

My results provide evidence that womens education does significantly reduce

fertility in developing countries, which has a significant impact on reducing the young

male share of the population. However, in the developing world, there is no evidence

that a reduction in the male share will reduce terrorism. In order to examine the

possibility that terrorism is related to the native young male share instead of the total

Because much of my research is motivated by observation of low education high


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Because much of my research is motivated by observation of low education, high

young male share, and high terrorism in the Middle East, I also analyze a subset of my

data, specific to a broadly defined Middle East/North Africa region that extends from

Algeria to Pakistan and north through much of Central Asia. In these countries, the basic

relationship between womens education and fertility is weaker, which weakens the

causal chain between womens education and terrorism. Results for the regression of

young male share on a lagged value of fertility are as expected: because immigration is

particularly high in this region, fertility is not a good predictor of total young male share

in this region. Finally, analyses of the effects of total young male share and native young

male share in this region also support the negative finding that these variables do not

affect terrorism.

In order to strengthen the results presented here, it would be useful to improve

upon the data set. While terrorism data is often hard to collect, the ITERATE data set

contains a wide array of information about international terrorist events. Unfortunately,

economic and political data on developing countries are not as complete. Further

exploration of available control variable data would be helpful, especially for countries in

the Middle East/North Africa region. Expanding the data set would lend more power to

the statistical estimates, which would help identify the various causes and correlates of

terrorism.

While terrorism has been experienced worldwide, many of the characteristics and


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References


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Appendix: Summary and Regression Tables


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Appendix: Summary and Regression Tables

Table 1: Percent of Population that is Male, Age 15-24

Region 1950 1960 1970 1980 1990 2000Middle East 19.9 17.6 19.0 19.7 19.6 19.5

Africa 19.1 18.5 18.7 19.3 19.5 20.6

Asia 19.1 17.5 18.6 19.6 20.3 17.9Europe 18.4 16.2 16.8 17.3 15.3 14.6

Northern America 15.0 13.8 17.8 19.4 15.2 14.3

Australia/New Zealand 15.9 15.3 18.2 18.5 17.5 15.8Latin America & the

Caribbean

18.7 17.8 18.9 20.3 20.1 19.9

Less Developed Regions 19.0 17.6 18.7 19.8 20.3 18.7

More Developed Regions 17.8 15.9 17.4 17.5 15.3 14.4

World 18.6 17.1 18.3 19.3 19.2 17.8 See Table 14 for list of countries included in each region.

Source: United Nations World Population Prospects Database

Table 2: Years with the Fewest and Most Terrorist Events


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Rank Year Number of Events

1 1991 578

2 1993 552

3 1986 5334 1985 524

5 1980 523

|

|

179 2000 167180 2002 130

181 1968 123182 1998 95

183 2001 52

Source: ITERATE Database

Table 3: Top Five Countries Producing Terrorists

Rank CountryTerrorist EventsCommitted by Nationals

1 Northern Ireland (United Kingdom) 596

2 Colombia 420

3 Iran 3484 Lebanon 340

5 Turkey 295

Source: ITERATE Database

Table 4: Regional Means for Main Variables


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Variable Time Period All

Countries

Developing

Countries

Middle East/

North Africa

Adult Female Literacy 1980-2000 69.56 60.15 57.13

Fertility 1980-2000 3.75 4.55 4.37

Young Male Share 1980-2000 9.25 9.87 9.66

Terrorist Incidents 1980-2000 8.23 7.56 13.12

Terrorist Casualties 1980-2000 26.82 28.58 61.81

Fertility (lagged) 1960-1980 5.00 6.01 6.18

Sex Ratio (lagged) 1960-1980 1.03 1.02 1.05

Infant Mortality (lagged) 1960-1980 82.58 105.82 99.53

See Table 14 for list of countries included in each region.

Table 5: Regional Means for Control Variables

Variable Time Period AllCountries

DevelopingCountries

Middle East/North Africa

Female percentage of 1980-2000 37.34 35.85 27.10

labor force

Infant Mortality 1980-2000 49.38 64.85 48.75

GDP per capita 1980-2000 7.01 2.53 5.05

(in thousands)GDP per capita growth 1980-2000 1.14 0.96 0.59

Muslim share of 1980-2000 22.58 30.43 80.28

populationLiteracy 1980-2000 74.38 66.64 66.72

Political Rights 1980-2000 3.54 4.31 5.01

Table A4: Summary Statistics for Main Variables Developing Countries


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Variable Time

Period

Mean Standard

Deviation

Minimum Maximum

Adult Female Literacy 1980-2000 60.15 27.49 2.67 95

Fertility 1980-2000 4.55 1.69 1.18 9.93

Young Male Share 1980-2000 9.87 0.73 6.46 12.47

Terrorist Incidents 1980-2000 7.56 18.49 0 169

Terrorist Casualties 1980-2000 28.58 86.70 0 1098

Fertility (lagged) 1960-1980 6.01 1.36 1.74 10.13

Sex Ratio (lagged) 1960-1980 1.02 0.03 0.94 1.14

Infant Mortality (lagged) 1960-1980 105.82 45.78 10.04 263.2

Table A5: Summary Statistics for Control Variables Developing Countries

Variable Time

Period

Mean Standard

Deviation

Minimum Maximum

Female percentage of 1980-2000 35.85 9.69 5.10 52.46

labor force

Infant Mortality 1980-2000 64.85 38.47 3.14 191.20

GDP per capita 1980-2000 2.53 3.99 0.05 35.40

(in thousands)GDP per capita growth 1980-2000 0.96 6.29 -51.94 100.84

Muslim share of 1980-2000 30.43 38.32 0 100population

Literacy 1980-2000 66.64 23.26 7.95 95

Political Rights 1980-2000 4.31 1.94 1 7

Civil Liberties 1980-2000 4.39 1.55 1 7

Table A6 - Regression 1: Fertility and Adult Female Literacy, OLS


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Specification

(1) (2)Dependent variable: fertility fertility

Fixed effects: none year and country

Adult Female Literacy -0.025 -0.041(0.006)*** (0.015)***

Female percentage of -0.033 -0.011

labor force (0.013)*** (0.019)Infant Mortality 0.025 0.018

(0.004)*** (0.005)***

GDP per capita 0.014 0.041(0.022) (0.016)**

GDP per capita -0.016 -0.001

growth (0.008)** (0.003)

Muslim share of -0.004 0.062population (0.003) (0.024)**

No. of countries 90 90R2

0.733 0.973

Observations 1702 1702

Standard errors in parentheses are robust and clustered at the country level. 1, 2 or 3 stars refer to

significance at the 10, 5 or 1% levels, respectively.

Table A7 Regression 2: Young Males and Fertility, OLS

Specification(1) (2)

Dependent variable: young male share young male share

Fixed effects: no fixed effects year and country

Fertilityt-20 0.307 0.603

(0.061)*** (0.081)***Sex Ratiot-20 6.622 -1.817

(1.850)*** (4.002)

Infant Mortalityt-20 -0.002 0.009(0.002) (0.006)

GDP per capita -0.063 -0.077

(0.017)*** (0.079)

Literacy 0 012 0 017

Table A8 Regression 3: Terrorism and Young Males, OLS


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Specification

(1) (2) (3) (4)Terrorismmeasure:

incident count casualties incident count casualties

Fixed effects: none noneyear andcountry

year andcountry

Young Males 0.458 -11.850 -3.464 -27.654

(1.102) (8.712) (1.403)** (19.987)GDP per capita -0.647 -1.575 -0.726 -6.443

(0.242)*** (1.106) (0.511) (5.978)Literacy 0.182 0.639 0.312 3.945

(0.075)** (0.317)** (0.279) (2.578)

Political Rights -2.875 -15.656 -0.093 -7.968

(1.027)*** (4.587)*** (0.807) (3.310)**

Civil Liberties 3.520 23.024 0.331 12.965(1.262)*** (6.481)*** (0.957) (4.810)***

Muslim share of 0.101 -2.773 0.314 5.169

population (0.058)* (19.149) (0.275) (3.284)Ethno-linguistic 3.254 0.422 --- ---

fractionalization (5.442) (0.197)**

No. of countries 90 90 90 90

R2

0.070 0.074 0.643 0.440

Observations 1702 1702 1702 1702

Standard errors in parentheses are robust and clustered at the country level. 1, 2 or 3 stars refer to

significance at the 10, 5 or 1% levels, respectively.

Tab

The Effect of Womens Education on Terrorism

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